• Explainable AI by defeasible rules

    Project ID: STAI-CDT-2021-IC-17
    Themes: Argumentation, Logic
    Supervisor: Francesca Toni

    The field of explainable AI (XAI) is a particularly active area of research at the moment whose goal is to provide transparency to the decisions of traditionally more opaque machine learning techniques. Being able to assess...

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  • A framework for verifying the safety and trustworthiness of AI systems

    Project ID: STAI-CDT-2021-KCL-1
    Themes: Argumentation
    Supervisor: Oana Cocarascu

    AI systems are increasingly used to aid human-decision making. Whilst AI systems have seen successes in a variety of tasks achieving highly-accurate results, oftentimes these systems tend to lack explainability and...

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  • Debating Ethics: Using Natural Language Datasets to Support Human and AI debate

    Project ID: STAI-CDT-2021-KCL-2
    Themes: Argumentation
    Supervisor: Oana Cocarascu, Sanjay Modgil

    Artificial intelligence (AI) algorithms, including machine learning and deep learning techniques, have been applied with success to a plethora of natural language datasets and tasks, including argumentative text. The goal...

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  • Data-Driven and Explainable Discrete Optimization for Effective Transportation in Healthcare

    Project ID: STAI-CDT-2021-KCL-3
    Themes: AI Planning, Argumentation, Scheduling
    Supervisor: Dimitrios Letsios

    This project aims to contribute to the development of safe and trusted, artificially intelligent transportation in healthcare. The London Ambulance Service (LAS) operates more than 1100 ambulances to respond to medical...

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  • Argumentation-based Interactive Explainable Scheduling

    Project ID: STAI-CDT-2021-IC-11
    Themes: Argumentation
    Supervisor: Ruth Misener

    AI is continuing to make progress in many settings, fuelled by data availability and computational power, but it is widely acknowledged that it cannot fully benefit society without addressing its widespread inability to...

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